diff --git a/.gitea/workflows/backed_build-image.yaml b/.gitea/workflows/backend_build-image.yaml
similarity index 95%
rename from .gitea/workflows/backed_build-image.yaml
rename to .gitea/workflows/backend_build-image.yaml
index 5542a1f..1552c03 100644
--- a/.gitea/workflows/backed_build-image.yaml
+++ b/.gitea/workflows/backend_build-image.yaml
@@ -2,6 +2,8 @@ on:
pull_request:
branches:
- main
+ paths:
+ - backend/**
name: Build and push docker image
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 79dbe76..01d58a2 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -6,6 +6,11 @@ COPY Pipfile Pipfile.lock .
RUN pip install pipenv
RUN pipenv install --deploy --system
-COPY . /src
+COPY src src
-CMD ["pipenv", "run", "python", "/app/src/main.py"]
+EXPOSE 8000
+
+# Set environment variables used by the deployment. These can be overridden by the user using this image.
+ENV NUM_WORKERS=1
+
+CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
diff --git a/backend/src/constants.py b/backend/src/constants.py
new file mode 100644
index 0000000..515a040
--- /dev/null
+++ b/backend/src/constants.py
@@ -0,0 +1,9 @@
+from pathlib import Path
+
+
+PARAMETERS_DIR = Path('src/parameters')
+AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
+LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
+OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
+
+OSM_CACHE_DIR = Path('cache')
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 618e169..39fa404 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -1,9 +1,11 @@
import math as m
import json, os
+import yaml
from typing import List, Tuple, Optional
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+import constants
from structs.landmarks import Landmark, LandmarkType
from structs.preferences import Preferences, Preference
@@ -13,34 +15,40 @@ NATURE = LandmarkType(landmark_type='nature')
SHOPPING = LandmarkType(landmark_type='shopping')
+
# Include the json here
# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
+ with constants.AMENITY_SELECTORS_PATH.open('r') as f:
+ amenity_selectors = yaml.safe_load(f)
+
+ with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+ # even though we don't use the parameters here, we already load them to avoid unnecessary io operations
+ parameters = yaml.safe_load(f)
- l_sights, l_nature, l_shop = get_amenities()
L = []
# List for sightseeing
if preferences.sightseeing.score != 0 :
- L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
+ L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, coordinates, parameters)
correct_score(L1, preferences.sightseeing)
L += L1
# List for nature
if preferences.nature.score != 0 :
- L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
+ L2 = get_landmarks(amenity_selectors['nature'], NATURE, coordinates, parameters)
correct_score(L2, preferences.nature)
L += L2
# List for shopping
if preferences.shopping.score != 0 :
- L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
+ L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, coordinates, parameters)
correct_score(L3, preferences.shopping)
L += L3
L = remove_duplicates(L)
- return L, take_most_important(L)
+ return L, take_most_important(L, parameters)
"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
@@ -69,37 +77,8 @@ def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float
return remove_duplicates(L), take_most_important(L)
"""
# Helper function to gather the amenities list
-def get_amenities() -> List[List[str]] :
-
- # Get the list of amenities from the files
- sightseeing = get_list('/amenities/sightseeing.am')
- nature = get_list('/amenities/nature.am')
- shopping = get_list('/amenities/shopping.am')
-
- return sightseeing, nature, shopping
-
-
-# Helper function to read a .am file and generate the corresponding list
-def get_list(path: str) -> List[str] :
-
- with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
- content = f.readlines()
-
- amenities = []
- for line in content :
- amenities.append(line.strip('\n'))
-
- return amenities
-
-
# Take the most important landmarks from the list
-def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
-
- # Read the parameters from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
- parameters = json.loads(f.read())
- N_important = parameters['N important']
-
+def take_most_important(L: List[Landmark], parameters: dict, N: int = 0) -> List[Landmark]:
L_copy = []
L_clean = []
scores = [0]*len(L)
@@ -127,7 +106,8 @@ def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
for i, elem in enumerate(L_copy) :
scores[i] = elem.attractiveness
- res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
+
+ res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(parameters['N_important']-N):]
for i, elem in enumerate(L_copy) :
if i in res :
@@ -220,19 +200,25 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
return min_lat, min_lon, max_lat, max_lon
-def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
+def get_landmarks(
+ list_amenity: list,
+ landmarktype: LandmarkType,
+ coordinates: Tuple[float, float],
+ parameters: dict
+) -> List[Landmark]:
- # Read the parameters from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
- parameters = json.loads(f.read())
- tag_coeff = parameters['tag coeff']
- park_coeff = parameters['park coeff']
- church_coeff = parameters['church coeff']
- radius = parameters['radius close to']
- bbox_side = parameters['city bbox side']
+
+ # # Read the parameters from the file
+ # with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+ # parameters = json.loads(f.read())
+ # tag_coeff = parameters['tag coeff']
+ # park_coeff = parameters['park coeff']
+ # church_coeff = parameters['church coeff']
+ # radius = parameters['radius close to']
+ # bbox_side = parameters['city bbox side']
# Create bbox around start location
- bbox = create_bbox(coordinates, bbox_side)
+ bbox = create_bbox(coordinates, parameters['city_bbox_side'])
# Initialize some variables
N = 0
@@ -269,11 +255,11 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
# Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
if amenity == "'amenity'='place_of_worship'" :
- score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)
+ score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])
elif amenity == "'leisure'='park'" :
- score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)
+ score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])
else :
- score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+ score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff']
if score is not None :
# Generate the landmark and append it to the list
diff --git a/backend/src/main_example.py b/backend/src/main_example.py
deleted file mode 100644
index 1a49305..0000000
--- a/backend/src/main_example.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import fastapi
-from dataclasses import dataclass
-
-
-@dataclass
-class Destination:
- name: str
- location: tuple
- attractiveness: int
-
-
-
-d = Destination()
-
-
-
-def get_route() -> list[Destination]:
- return {"route": "Hello World"}
-
-endpoint = ("/get_route", get_route)
-end
-if __name__ == "__main__":
- fastapi.run()
diff --git a/backend/src/optimizer.py b/backend/src/optimizer.py
index b346a8a..68362c6 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/optimizer.py
@@ -1,13 +1,13 @@
import numpy as np
import json, os
+import yaml
from typing import List, Tuple
from scipy.optimize import linprog
from math import radians, sin, cos, acos
-from shapely import Polygon
from structs.landmarks import Landmark
-
+import constants
# Function to print the result
def print_res(L: List[Landmark], L_tot):
@@ -161,10 +161,11 @@ def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float
# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
def init_ub_dist(landmarks: List[Landmark], max_steps: int):
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- detour = parameters['detour factor']
- speed = parameters['average walking speed']
+ # Read the parameters from the file
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+ detour = parameters['detour_factor']
+ speed = parameters['average_walking_speed']
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
c = []
@@ -194,9 +195,9 @@ def respect_number(L:int, A_ub, b_ub):
b_ub.append(1)
# Read the parameters from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- max_landmarks = parameters['max landmarks']
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+ max_landmarks = parameters['max_landmarks']
A_ub = np.vstack((A_ub, ones*L))
b_ub.append(max_landmarks+1)
@@ -300,13 +301,14 @@ def respect_order(N: int, A_eq, b_eq):
# Computes the time to reach from each landmark to the next
-def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+def link_list(order: List[int], landmarks: List[Landmark]) -> List[Landmark]:
# Read the parameters from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- detour_factor = parameters['detour factor']
- speed = parameters['average walking speed']
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+
+ detour_factor = parameters['detour_factor']
+ speed = parameters['average_walking_speed']
L = []
j = 0
@@ -329,10 +331,11 @@ def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
# Read the parameters from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- detour_factor = parameters['detour factor']
- speed = parameters['average walking speed']
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+
+ detour_factor = parameters['detour_factor']
+ speed = parameters['average_walking_speed']
L = []
j = 0
diff --git a/backend/src/parameters/amenity_selectors.yaml b/backend/src/parameters/amenity_selectors.yaml
new file mode 100644
index 0000000..43e8220
--- /dev/null
+++ b/backend/src/parameters/amenity_selectors.yaml
@@ -0,0 +1,26 @@
+nature:
+ - "'leisure'='park'"
+ - "geological"
+ - "'natural'='geyser'"
+ - "'natural'='hot_spring'"
+ - "'natural'='arch'"
+ - "'natural'='volcano'"
+ - "'natural'='stone'"
+ - "'tourism'='alpine_hut'"
+ - "'tourism'='viewpoint'"
+ - "'tourism'='zoo'"
+ - "'waterway'='waterfall'"
+
+shopping:
+ - "'shop'='department_store'"
+ - "'shop'='mall'"
+
+sightseeing:
+ - "'tourism'='museum'"
+ - "'tourism'='attraction'"
+ - "'tourism'='gallery'"
+ - "historic"
+ - "'amenity'='planetarium'"
+ - "'amenity'='place_of_worship'"
+ - "'amenity'='fountain'"
+ - "'water'='reflecting_pool'"
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
new file mode 100644
index 0000000..3cf1c76
--- /dev/null
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -0,0 +1,6 @@
+city_bbox_side: 10
+radius_close_to: 27.5
+church_coeff: 0.6
+park_coeff: 1.5
+tag_coeff: 100
+N_important: 40
diff --git a/backend/src/parameters/landmarks_manager.params b/backend/src/parameters/landmarks_manager.params
deleted file mode 100644
index 942c214..0000000
--- a/backend/src/parameters/landmarks_manager.params
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "city bbox side" : 10,
- "radius close to" : 27.5,
- "church coeff" : 0.6,
- "park coeff" : 1.5,
- "tag coeff" : 100,
- "N important" : 40
-}
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer.params b/backend/src/parameters/optimizer.params
deleted file mode 100644
index 18ca240..0000000
--- a/backend/src/parameters/optimizer.params
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "detour factor" : 1.4,
- "average walking speed" : 4.8,
- "max landmarks" : 10
-}
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
new file mode 100644
index 0000000..b6dca0e
--- /dev/null
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -0,0 +1,3 @@
+detour_factor: 1.4
+average_walking_speed: 4.8
+max_landmarks: 10
diff --git a/backend/src/refiner.py b/backend/src/refiner.py
index 282f2f9..a292b73 100644
--- a/backend/src/refiner.py
+++ b/backend/src/refiner.py
@@ -1,7 +1,8 @@
from collections import defaultdict
from heapq import heappop, heappush
from itertools import permutations
-import os, json
+import os
+import yaml
from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
from typing import List, Tuple
@@ -10,6 +11,7 @@ from math import pi
from structs.landmarks import Landmark
from landmarks_manager import take_most_important
from optimizer import solve_optimization, link_list_simple, print_res, get_distance
+import constants
def create_corridor(landmarks: List[Landmark], width: float) :
@@ -122,12 +124,12 @@ def total_path_distance(path: List[Landmark], detour, speed) -> float:
def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
-
- # Read from data
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- detour = parameters['detour factor']
- speed = parameters['average walking speed']
+ # Read the parameters from the file
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+
+ detour = parameters['detour_factor']
+ speed = parameters['average_walking_speed']
# Step 1: Find 'start' and 'finish' landmarks
start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
@@ -174,8 +176,10 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
for landmark in all_landmarks :
if is_in_area(area, landmark.location) and landmark.name not in visited_names:
second_order_landmarks.append(landmark)
-
- return take_most_important(second_order_landmarks, len(visited_landmarks))
+
+ with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+ return take_most_important(second_order_landmarks, parameters, len(visited_landmarks))
@@ -195,10 +199,10 @@ def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[L
def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
- # Read from the file
- with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
- parameters = json.loads(f.read())
- max_landmarks = parameters['max landmarks']
+ # Read the parameters from the file
+ with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+ parameters = yaml.safe_load(f)
+ max_landmarks = parameters['max_landmarks']
if len(base_tour)-2 >= max_landmarks :
return base_tour